Active Labor Programs in Romania

8/8/2019 Active Labor Programs in Romania

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I. Introduction

Even though open unemployment was practically non-existent in Romania prior to 1989,with the introduction of social, political, and economic reforms, labour surplus soared. Therestructuring process affected many workers who saw the value of their human capitaldeteriorate, and struggled into finding new job or business opportunities. The Romaniangovernment soon recognized the urgency of developing social safety programs and active labour

market programs (ALMPs hereafter) to help the unemployed during this transition period. Thus,in the early 1990s, the Ministry of Labor and Social Protection combined social insurance andmeans-tested income support with active policies aimed at increasing labor demand for youths,improving matching by providing retraining for unemployed individuals, and stimulating jobcreation through credits to businesses. However, the extent of these active programs remainedlimited, as discussed in Earle and Pauna, 1998. And it is not until the late 1990s that theRomanian government launched the real start of active programs on a significant scale bysigning a loan agreement with the World Bank.

In this paper, we evaluate the effectiveness and efficiency of four active labor marketprograms that were implemented through this agreement in Romania at the end of the 1990s.These programs were: (1) training and retraining (TR), (2) self-employment assistance (SE), (3)public employment (PE), and (4) employment and relocation services (ER). The objective of theevaluation was to inform the Romanian Ministry of Labor and Social Protection and theNational Agency for Employment and Vocational Training on the impacts of these programson the employment experiences and earnings prospects of its participants as compared to the

outcome if the individual had continued to search for a job as openly unemployed, that is, notparticipating in any of the ALMPs under evaluation. We also considered program costs andprovide information as to whether the programs were cost-effective. The focus is on the directeffects of the programs; no attempt is made to assess the general equilibrium implications.1

Therefore, this studys contribution to the Romanian and the international literature isthreefold. First, by providing an evaluation of the effects of four ALMPs implemented inRomania in the late 1990s, this paper increases our knowledge on what experiences work intransition economies. Second, we argue that our data is unusually rich for studies conductedin transition economies, which allows us to address the selection issues in a reasonable way.Third, we explore the net social benefits of those ALMPs found effective.

Our analysis is based on a follow-up survey specifically designed and collected for thisevaluation The most important reasons for using survey data instead of administrative data


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after the programs started. About half of this sample (2,047 persons) were ALMP participantswhose ALMP contract began in 1999. The other half did not participate in any of the fourALMPs under analysis.

We base our analysis on the conditional independence assumption (CIA), and use a kernel-based matching estimator to estimate the average treatment effect. A part of the paper is devotedto discussing the plausibility of the CIA in this context. One of the biggest challenges when

evaluating ALMPs in transition economies is the quality and quantity of datasee Kluve,Lehmann and Schmidt, 1999, or Earle and Pauna, 1996, among others, for discussion on the poorquality of ALMPs data in transition economies. We argue that our data contains importantbaseline informationin particular, pre-treatment earnings, employment history and experienceinformationmaking the CIA assumption more plausible.

Our analysis of program impacts reveals that three of the four programs (TR, SE and ER)had success in improving participants' economic outcomes and were cost-beneficial from

societys perspective. We find that ER succeeded in increasing the likelihood of participantsemployment and their earnings, and reducing the likelihood of receiving unemploymentbenefits. We also find that SE improved its participants employment prospects, although itdid not have a significant impact on their earnings; and that TR increased the earnings of itsparticipants and reduced the likelihood of receiving unemployment benefits. In contrast, ouranalysis reveals that PE was found detrimental for the employment prospects of itsparticipants.

While the literature on evaluations of ALMPs in developed market economies is vast, theevidence on transition countries is scarcer.2 Recently, several studies have studied theeffectiveness of ALPMs in transition economies, like Bosnia and Herzegovina (Benus, Rude,and Patrabansh, 2001), Bulgaria (Walsh, Kotzeva, Dolle, and Dorenbos, 2001), CzechRepublic (Boeri and Burda, 1996, Terrell and Sorm, 1999), Hungary (Gill and Dar, 1995,OLeary 1995, OLeary, 1998a, and OLeary, Kolodziejczyk, and Lazar, 1998), Macedonia(World Bank, 2002), Poland (Kluve, Schmidt and Lehmann, 1999, Kluve Lehman andSchmidt, 2002, Puhani 1998, OLeary, 1998b, and OLeary, Kolodziejczyk, and Lazar, 1998),Slovak Republic (Lubyova and Van Ours, 1999), Slovenia (Vodopivec, 1999), and Ukraine(Kupets, 2000). Overall, our results are consistent with earlier findings, as discussed in ourfindings section, section VI.3

This paper is organized as follows. The next two sections present background informationon Romanian labor market development and the set up of the ALMPs under evaluation


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Section five discusses the economic evaluation strategy and the empirical implementation.Section six displays the results. Section seven discusses heterogeneity among individuals andsensitivity analyses. Section eight concludes with the cost-benefit analysis.

II. The Economic Context

Romanias transition to a market economy has been slow and painful partly as a result ofits stop-and-go approach to the restructuring process. Since the 1989 Revolution, successivegovernments have adopted a cautious approach to market-oriented reforms. This slow pace ofreformrelative to some of its neighbours in Central Europedelayed needed structuralchanges and added greater difficulties to the already unfavourable set of initial conditionsinherited from the previous regime.

As shown in Figure 1, after an initial economic contraction in the early 1990s due to the

increase of external competition and the abolition of the Council of Mutual Economic Assistance(CMEA), Romania experienced a partial economic recovery beginning in 1992, similar to theone observed in leading transition economies in Central Europe. However, in contrast with theseleading economies, Romania lived a second period of economic decline beginning in 1996,which was mainly caused by the lack of enterprise restructuring. In the second half of 1996,Romanias authorities took a series of decisions with the aim of accelerating the privatization,restructuring and liquidation of unprofitable business. However, the recovery was slow and didnot produced significant economic results until the year 2000. Since then the Romania economy

has grown at an average of 4 or 5 per cent per year.

Figure 1

Romania Economic Indicators, 1990-2001

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mploymentRate

30 0

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GDPIndex


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reached over 10 percent of the labor force in 1994. The unemployment rate then fell temporarilyduring 1995 and 1996, only to rise rapidly thereafter reaching 11.5 percent in 1999. Since then,the registered unemployment rate has fallen gradually to 9 percent of the labor force in 2001.

Data on registered unemployment in Romania understate the real problem with dislocatedworkers for at least the following three reasons. First, during the 1990s the increase in openunemployment was contained by Romanias policy approach of limiting job destruction by

adjusting through real wages, combined with a series of early retirement programs. Even thoughthese two policies succeeded in limiting the increase in registered unemployment, it pushedworkers out of the labor force and into low productivity jobs, primarily in agriculture. Second, ahigh share of Romanias employment is in subsistence agriculturethe share of agriculturalemployment in Romania in 2001 was 42 percent of total employment (up from 28 percent oftotal employment in 1989). This suggests the existence of a two-tier system consisting of a largenumber of people involved in low productivity jobs and subsistence agriculture coexisting withlarge, potentially profitable but unreformed, farms. And third, the existence of borderline

employment categories to measure employment in Romania substantially influences keyindicators of labor market performance and suggest that many individuals in Romania are readyto work but that the limited opportunities prevent them from doing so.4

III. Labour Market Programs

Even though open unemployment was practically non-existent in Romania prior to 1989,

with the introduction of social, political, and economic reforms, the emergence of labor surplussoared. The Romanian government soon recognized the urgency of developing programs to helpthe unemployed during this transition period. Thus, as early as 1991, the Ministry of Labor andSocial Protection adopted the Romanian Unemployment Program. This program was not a puresocial insurance program since it contained provisions for means-testing.5 According to thisprogram, unemployed individuals were eligible for financial support through unemploymentbenefits, allowance for vocational integration, and support allowance. The conditions to beeligible were the following: to be registered at the local Labor Office, to be aged eighteen andover, to have an income less than half of the indexed national minimum wage, to be unemployeddue to liquidation or a lay-off, and to have been employed at least six months during the lasttwelve months, or recent graduate from school or university unable to find suitable employment.Unemployment benefits were paid for a maximum duration of nine months. The level of thesebenefits ranged from 50 to 60 percent of the average monthly salary during the last three monthsof employment for laid off workers For new entrants allowances for vocational integration


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varied by the level of education and years of experience for those with prior work experience.After exhausting unemployment benefits, those who remained unemployed received a supportallowance (of 60 percent of the indexed minimum wage) for a maximum period of 18 months.6

In the early 1990s, Romania also adopted several active policies aimed at increasing labordemand for youths, improving matching by providing retraining for many categories ofunemployed individuals, and stimulating job creation through credits to businesses.7 However,

the extent of these active programs remained limited, as discussed in Earle and Pauna, 1998.Thus, the need for additional and more diversified measures enabling to support employmentemerged progressively and became particularly urgent after 1996-1997 when privatization andthe restructuring accelerated and resulted in massive layoffs.

In the late 1990s, the Romanian government launched the real start of active programs on asignificant scale by signing a loan agreement with the World Bank. The focus of the presentpaper is to evaluate the effectiveness of the four ALMPs implemented under this agreement: (1)

training and retraining (TR), (2) small business assistance (SE), (3) public employment (PE), and(4) employment and relocation services (ER).

Implementation of ALMPs

Implementation of ALMPs began in 1997 by the National Agency for Employment andVocational Training and the county agencies for Employment and Vocational Training.

County level Agencies for Employment and Vocational Training designed and implementedActive Labor Measures for displaced workers. However, these services were not provided bythe county agencies themselves, but were contracted out to public or private service providers.The county agencies were responsible for the public announcements of the tenders,conducting the tendering process, and contracting out the ALMPs.

Contracts to service providers were awarded with built-in incentives to improve labor marketimpact such as negotiated levels of job placement and business start-up, with financial incentivesto meet objectives and disincentives if objectives were not met. Thus, service providers werelikely to select those unemployed individuals most likely to succeed in completing their programand accessing employment. As we shall see in section IV, this will cause selection bias due to acorrelation of individual program participation with the outcomes under investigation.


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The four programmes were clearly differentiated as evident from the description of theirkey characteristics presented in Table 1. While SE and ER offered services aiming tofacilitate business start-ups for displaced entrepreneurs (the former), and job placement forrecently unemployed workers (the latter), the other two programs were targeted to moredifficult populations. TR offered vocational training, general education and literacy skills tothose who lacked these basic skills or needed to learn new marketable ones. While PE wasfrequently considered as fully subsidised labour, and was mainly offered in those regions with

the least economic opportunities.

The maximum length of these programs also varied, and ranged between six monthsforpublic service employment, and a yearfor employment and relocation services. However, inpractice, the length was considerably shorter than the established maximum duration.Participation in TR, PE, and ER increased the period in which individuals could receivebenefits by the length of the program (in the case of TR and PE), and by a maximum of twomonths in the case of ER, adding an incentive for certain individuals to participate in the

programs.

The requirements to service providers are key to understanding how the selection into thedifferent programmes was done. In all programs, service providers had to agreed to aminimum negotiated job placement rate of an average of its participants. This rate was thelowest for SE programs, which had to agree to a negotiated business start-up rate of at least 5percent of clients initially contacted, and highest for TR services, in which case localorganizations proposing training programs had to show evidence of demand for trained

workers and agree to a minimum negotiated job placement rate of an average of at least 60percent. For the other two types of services, PW and ER, providers had to agree to negotiatedjob placement rates of at least 10 percent at the end of the program.

In addition, service providers had to demonstrate minimum capabilities to be serviceproviders, such as staff qualifications, facilities, financial viability, and placement capability,and they had to agree certain provisions on eligible costs, and limits to reimbursement perservice, as described in Table 2.

Finally, there were some requisites that prevented duplication of payment and services.First, individual clients could not receive income support payments (e.g., minimum wageduring TR or PE) if they were receiving other types of state financed income support, such asunemployment benefits. Second, individuals could not participate in both TR and PE. Andthird individuals were not allowed to participate more than once in a programme in a period


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The program with the largest number of clients (ER) provided assistance to 31,679individuals at an average cost of only 123.74 thousand lei per client (about 12US$ per client).In contrast, the PE served a much smaller number of clients (9,496); the cost per client for thisprogram was 2,915.77 thousand lei per client (about US$294 per client).8

Based on discussions with program implementation staff, we determined that contractsthat begun in 1999 most accurately reflect the operations of the ALMPs. Prior to 1999, the

ALMPs were new and some of the procedures were not fully implemented. Contracts thatbegun after 1999 may not be suitable for the evaluation since some may still be in operationor recently finished at the time of the survey and impacts from these contracts may not yet befully reflected in participants outcomes. Thus, our sample was drawn from contracts thatstarted during 1999.

IV. The Data and Descriptive Statistics

Data Source

This study uses data from a follow-up survey of registered unemployed specificallydesigned for this evaluation.9 We shied away from using existing data from the Ministry ofLabor and Social Protection for several reasons. First, we were concerned on the quantityand quality of the existing data. But, more importantly, data from the Ministry lacked severalkey variables needed for our analysis, such as earnings for both the employed and the self-

employed.10

Compared with existing data from the Ministry, our follow-up survey data providedmuch more detailed characteristics of the unemployed individuals, and observed their earningsand employment status at different points in time over a four-year period. Moreover, sinceofficial data is collected in the local labor offices, we were concerned that respondents would bemore reluctant to fully disclose their earnings to public authorities (as part of tax avoidancestrategy) than to trained interviewers from an outside independent agency.11

For these reasons, we contracted with a Romanian private survey firm, Institute ofMarketing and Polls (IMAS), to conduct field surveys during January-February 2002. The studygoal was to achieve over 4,000 respondents. Of the 5,735 individuals contacted for interviewing,about 70 percent responded. As is common in these type of studies, response rate was slightlyhigher for participants (72 percent) than for non participants (68 percent)


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Sample Selection

The data used in this study, a random sample of approximately 3,996 persons whoregistered at the Employment Bureau during 1999, was collected during January and February2002. About half of this sample, 2,047 persons, were ALMP participants whose ALMP

contract began in 1999. The other half did not participate in any of the four ALMPs underanalysis.

To obtain a representative sample of ALMP participants, we randomly selected, for eachof the four ALMPs, 10% of clients served in the fifteen counties with the largest number ofclients served in 1999.12 These fifteen counties (judets) represented 86% of all clients servedin 1999, and a broad spectrum of the Romanian economy with many sectors represented,including heavy industry, mining, and agriculture, among others. Moreover, these fifteen

judets included some of the poorest judets in Romania (Botosoni and Vaslui -- north-eastregion) as well as some judets with substantial natural resources and highly developedindustries (Cluj and Maramures -- north-west region).

The other half of the samplethe potential comparison groupwere 1,949 persons whowere registered at the Employment Bureau around the same time and in the same county thanparticipants but who had notparticipated in an ALMP. To select non-participants, we firstdetermined, for each of the four ALMPs, the number of participants that were selected for the

participant sample in each of the judets. Next, in each county and for each ALMP, werandomly selected an equal number of non-participants from the same Employment Bureauregister list.

The timing of events, shown in Figure 2, goes as follows. Some of the workers registeredat the Employment Bureau during 1999 received services from one of the four ALMPsdescribed above. The rest of the workers did not receive any of these services. Although it ispossible that some of the program participants may have continued to receive services duringthe year 2000 (since the maximum duration of the ALMPs varied between 6 and 12 months),it is quite unlikely since, in practice, the length of these programs was considerably shorter.During January and February of 2002, we interviewed the selected sample of participants andnon-participants. All interviewed persons were asked three types of questions: (1) questionson employment and earnings at the time of the survey, (2) retrospective questions onemployment and earnings during the years 2000 and 2001 and (3) retrospective questions on


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all of the observations available for each of the different outcome variables. However, inorder to work with the same sample in the whole paper we restricted our sample to have alldata available.

Figure 2

TIMING OF EVENTS

During 1999 2000 2001 January/February 2002

Displaced workersregistered atEmployment Bureau.Some participated inALMP, some did not.

Workers worked or looked for employment.During 2000, some of the ALMP participants mayhave continued to receive services.

Workers are interviewed

Descriptive Statistics

Tables 4 and 5 display descriptive statistics for socio-economic variables for thedifferent subsamples that are defined by treatment status. The descriptive statistics conformto our expectations that different types of displaced workers participated in the differentALMPs. The results are summarized below.

Clearly, participants in PE are the most disadvantaged among the unemployed both in

terms of level of education and employment history. They are the least educated, with onefifth of them having only primary school education. And they have the worse employmentprospects since almost two thirds of them were not employed in 1998, and their averageunemployment spell in 1998 was 9 months. Moreover, these participants are also the mostlikely to live in rural or small urban areas with high unemployment. This is in line with theidea that public employment is considered fully subsidised labor, and that it is offered mainlyin those regions with the least economic opportunities.

On the other hand, participants in TR are the youngest among the four ALMPs with anaverage age of 39.1 years old and less than one fifth older than 45 years old. This isconsistent with the idea that substantive human capital investments are more beneficial thelonger the productive period of the recipients. However, the fact that participants in TRappear to have the highest predisposition for training with almost one fifth of this groupparticipating in training in 1998 combined with their young age low education levelalmost


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In contrast, participants in SE and in ER have relatively more stable employmenthistory during 1998 than participants of the other two ALMPs, as three fourths reportedworking during 1998. There are, however, clear differences between these two groups.While, participants in SE tend to be more educated with two fifths of them holding a high-school degree and one fifth of them holding a university degree, participants in ER are morelikely to live in large urban areas.

Non-participants resemble the most to participants in ER and SE. However, theyexperienced considerably more stable and better paid employment during 1998. For example,two thirds of them were employed between 9 and 12 months in 1998. Moreover, with theexception of participants in public employment, which are predominantly males, non-participants have a higher share of men in their group.

V. Identification and Estimation

The Evaluation Problem

The evidence in the previous two sections shows that the ALMPs offered wereconsiderably different and targeted to individuals with different skills and labour marketexperiences. Thus, we focus our analysis on comparing the outcomes of two alternativestrategies available to displaced workers: to participate in a particular ALMP, or to continue

searching for a job as openly unemployed, following the framework suggested by Rubin(1973). 14

Let Yt denote the outcome when a person gets the treatment(in this case, participates inone of the four ALMPs described above), and Yc denote the outcome when a person does notparticipate in any of the ALMPs described above. Let D denote a binary assignment indicatorthat determines whether the individual gets the treatment (D=1) or not (D=0).

The average treatment effect on the treated (ATET) is defined as follows:

ATET=E(YtYc|D=1)=E(Yt|D=1)-E(Yc|D=1) (1)

The shorthand notation E(.|D=1) denotes the mean in the population of all individuals whoparticipate in an ALMP denoted by D 1


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jointly influence outcomes and participation decision, thenconditional on those factors (callthem X), the participation decision and the outcomes are independent. This property isexploited by the conditional independence assumption (CIA).

Is it Plausible to Assume Conditional Independence?

Our approach for meeting the CIA was to include in the matching process: (1)

characteristics influencing the decision to participate in ALMP, (2) baseline values of theoutcomes of interest, (3) variables influencing the outcomes of interest, and (4) variablesreflecting local labour market conditions, and regional differences in program implementationor local offices placement policies.

The characteristics, implementation, and utilization of the different ALMPs as well as thecharacteristics of their participants indicates that the level of education, experience, previousearnings, and pre-program unemployment history are important factors in determining whether

an individual will participate in any program, as well as in which of the programs. These factorsare also likely to influence the future labour market outcomes, and thus, in order for CIA to beplausible, they should be included in the estimation of the propensities.

Demographic characteristics, such as age and gender are also important determinantsof labour market prospects. Moreover, family composition and whether the person is thefamilys main wage earner are also likely to influence individuals decision to participate in aprogram.

We also include variables that capture the local labour market conditions. Thesevariables measure the different employment opportunities in the judets. In addition, sincedifferences in labour market conditions may favour a different mix of program andunemployment policies, these variables are also a proxy for different policy approaches acrosscounties.

Finally, we include county dummies to capture unobserved local aspects that are

likely to be correlated with program implementation and utilization, or local officesplacement policies, and thus relevant for program-joining decisions and individuals potentiallabour market performance.

Although at first sight and relative to studies conducted in developped countries, thesedata may not seem suficiently rich to observe all relevant factors we believe that our data is


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ALMP. We included the following variables in the probit models: male; age indicators;education indicators; earnings in 1998; 1998 experience (and its square); unemployed at leastnine months in 1998; length of unemployment during 1998 (and its square); only employedduring 1998; received training during 1998; 1998 judetunemployment rate; regions size; andregional indicators. Table 6 displays the estimation results of the four different binary probitsand provides a more exact description of the variables used in the analysis.

Second, we used the output from these selection models to estimate choiceprobabilities conditional on X (the so-called propensity scores) for each treatment andpotential comparison group member. We then imposed the common-support requirement toguarantee that there is an overlap between the propensity scores for each pair (see column 9 ofTable 7 for number of treated observations lost due to this requirement).

Third, for each treatment group member, we selected potential comparison groupmembers based on their propensity scores and their judet. The selection process was done

with replacement, so that a potential comparison group member could have been matched tomore than one treatment group member.16 In addition, the selection method used was kernel-based matching, which uses all of the comparison units within a predefined propensity scoreradius (or caliper of 1%). When there were multiple matches, each non-participant receiveda weight that reflects the number of successful matches within the caliper range.17

Similarity of the Treatment and Comparison Groups

Our goal was to select, for each of the four groups, a well-matched comparison group.A comparison group is well matched to a treatment if the estimated propensity score and thecollection of available baseline characteristics are not significantly different across the twogroups.

The results in Table 7 show statistical information on the quality of the match for eachof the four ALMPs. Columns 4 and 5 give an indication of how well baseline covariates

explain the participants probability before (column 4) and after (column 5) the match. Notsurprisingly, after the match the R2 decreases considerably. Similarly, the P-value of thelikelihood ratio test after matching shows that the null hypothesis of joint significance ofregressors is always rejected after the matching (column 6). The median bias after thematching is also considerably reduced (columns 7 and 8).


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months unemployed and receiving UB during the 2000-2001. Finally, ER had a positiveimpact on earnings: it increased average current monthly earnings by 57 thousand lei (or22%) and average monthly earnings during 2000-2001 by 87 thousand lei (or 28%) comparedto the earnings of non-participants.

We identified four studies that evaluated job search assistance and related employmentservices in transition economies. These studies took place in the Czeck Republic (Terrell and

Sorm, 1999), Macedonia (World Bank, 2002), Hungary and Poland (OLeary, 1998a).Similar to our findings, all of these studies found positive impacts of ER type of services onimproving employment prospects of participants. However, of the two evaluations thatestimated the impact of these services on earnings, only in Poland a positive effect wasfound.20

Self-Employment Assistance

We also find that SE improved its participants employment prospects. More

specifically, SE increased by 8.38 percentage points (or 12%) the likelihood of beingemployed for 6 months during the two-year period 2000-2001. This programme also reducedthe number of months participants were on average unemployed compared to non-participantsby almost two months, and the number of months receiving UB payments by almost onemonth. However, we did not find that SE increased the average monthly earnings of itsparticipants relative to non-participants. This lack of result could be explained byentrepreneurs under-reporting their earnings.

We identified three studies that evaluated self-employment assistance programs in thefollowing transition countries: Bulgaria (Walsh, Kotzeva, Dolle, and Dorenbos, 2001),Hungary and Poland (OLeary, 1998a). Consistent with our results, the three programsincreased the probability of reemployment. However, the evidence on earnings is mixed.While our study found no effect on earnings, the study from Hungary found a negative effect,while the study from Poland found a positive one. The Bulgarian program did not estimatethe impact of the program on earnings.

Training and RetrainingWe find that TR has a positive and large impact on the average usual monthly earnings

perceived during 2000-2001: it increased the earnings of participants by 165 thousand leirelative to the earnings of non-participants. This is equivalent to 58% higher earnings thannon-participants. TR also had an impact on the length of UB receipt, by making it practicallynon existent on average among its participants (see Table 9) Unfortunately due to the small


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Herzegovina (Benus, Rude, and Patrabansh, 2001), Bulgaria (Walsh, Kotzeva, Dolle, andDorenbos, 2001), Hungary (Gill and Dar, 1995, and OLeary 1995a), Macedonia (WorldBank, 2002), Poland (Kluve, Schmidt and Lehmann, 1999, Kluve Lehman and Schmidt, 2002,Puhani 1998, OLeary, 1998b), and Slovak Republic (Lubyova and Van Ours, 1999). Theseevaluations consistently find that training programs for unemployed workers improveparticipants employment prospects in the short-term. These results are in line with ourfindings, which point to positive (although insignificant) estimates on employment prospects,

and positive and significant impact on earnings and on unemployment insurance receipt.

Public Employment Programs

In contrast, we find that the PE program had a negative impact on employment, andlength of unemployment spell during the past two years. These detrimental effects are usuallyexplained by one or a combination of the following two explanations. First, participating inPE may be ineffective insofar as it does not rebuild human capital, boost search efforts orimprove the image of the long-term unemployed individual. Second, participation in PE is a

negative signal to the employer (Lehmann, 1995).

We identified the following eleven studies that evaluated public employmentprograms in transition economies: Bulgaria (Walsh, Kotzeva, Dolle, and Dorenbos, 2001),Hungary (OLeary, 1995, and OLeary, 1998(a)) Macedonia (World Bank, 2002b), Poland(Kluve, Schmidt and Lehmann, 1999, Kluve Lehman and Schmidt, 2002, Puhani 1998,OLeary, 1998b), Slovak Republic (Lubyova and Van Ours, 1999), Slovenia (Vodopivec,1999), and Ukraine (Olga, 2000). Overall, the results were mixed. While two evaluations in

Poland concluded, as in our study, that PE had a significant negative effect on exitingunemployment and future employment. Evaluations in Macedonia, Slovakia, Slovenia, andUkraine found that a positive employment effect disappeared if the worker did not find a jobimmediately after the program ended, suggesting that employers may be using PE as ascreening device before committing to formal employment. Finally, a small positive impacton employment was found in Bulgaria. However, this positive result was small compared toother ALMPs, and was achieved at a high unit cost.

VII. Heterogeneity and Sensitivity Analysis

In this section we explore the robustness of the results presented above. We firstexamine the sensitivity of the results to various estimators. Second, we examineheterogeneity among various types of individuals


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and non-participants, that is, we use our whole sample of non-participants regardless ofwhether their baseline characteristics resembled to those of participants. The second set ofresults (second column of Tables 11 through 14) is net impact estimates, which were adjustedfor demographic and regional differences, and earnings, employment, unemployment andtraining experiences in 1998 using multivariate ordinary least squares regression (when thedependent variable was continuous) or probit regression (when the dependent variable was abinary variable). The covariates included in the OLS and the probit estimations are the sameas those used to estimates the propensity scores in Table 10 and column 4 of Table 11. Thethird set of results are net impact estimates that were computed as simple differences betweenthe mean outcome of interest for the participant group and the mean outcome for a non-experimentally matched comparison group selected by the same propensity score methoddescribed in section V, however, we did not use any of the pre-earnings, pre-employment, andpre-unemployment history to match participants to non-participants. The fourth set of resultsare the estimators presented in section VI and Table 10.

The most obvious overall result in Table 10 through 14 is that the unadjusted impactestimates (column 1) are generally different from the other estimates (columns 2 through 4).For TR, SB and ER, the unadjusted impact estimates were better than the other ones,suggesting that operators may cream off the most qualified candidates among theunemployed for these particular types of programs. This finding is consistent with otheranalyses of ALMP in transition economies (OLeary,1998 and Kluve, Lehmann, and Schmidt,2001, among others). In contrast, adjusting for observable characteristics reduces thedetrimental employment and unemployment impact estimates on PE (Table 13). In particular,

the sensitivity of the results to the availability of different covariatesnot shown here, butavailable from the author upon requestindicates that information on the regional location ofparticipants and non-participants was essential when measuring this programs impacts,suggesting that programs like public employment may be used as a regional policy bybringing work to the workers, that is, creating job in high unemployment regions.

Comparing the gross impact estimates with the regression-adjusted estimates (column1 versus column 2) clearly reduces the positive impact of SE estimates and the negative

impact of PE estimates, reflecting that there is an over-representation of individuals withbetter observable characteristics in the group of SE participants and an over-representationof individuals with worse observable characteristics in the group of PE participants. In thecase of TR, we find that regression-adjusting the estimates reduces the positive effect of TRon current employment and earning outcomes, and increases it on employment and earningsoutcomes over the last two years Finally regression adjusting the estimates has little effect


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Moreover, the estimators are based on further parametric restrictions.

We observe substantial differences between the OLS and probit estimators on the onehand, and matching on the other for the cases of TR, ER, and PE. For TR and ER, we findthat the results obtained by matching reduce the positive employment effects of TR and SE.Whereas for PE we find that the matching estimates reflect a smaller negative impact onemployment outcomes for participantsthe negative impact disappears in the case ofoutcomes measuring current employment and earnings. These differences are presumablyexplained by the parametric restrictions underlying the OLS and probit estimations. Matchingallows for heterogeneity in the treatment effect in a more flexible way.

Comparing estimates from column 3 and 4 enable us to explore the importance ofcontrolling for pre-earnings, pre-employment, and pre-unemployment history. We find thatthese variables are very essential when measuring the effect of the different programs, asreflected by the fact that excluding them raises the size of impact estimates of all programs.

Heterogeneity among Types of Individuals

So far we have considered the average effects for the participants in the differentprogrammes. Since participants are heterogeneous, there may be differences in how theprogrammes affect different types of individuals. Therefore, we stratify the sample along thedimensions unemployment duration, type of region, age, education, and gender, and matchwithin strata. Below, we summarize the key findings (all subgroup estimates are displayed in

Tables 15 through 18).

Clearly, the most substantial (and significant) differences occur with respect to age,type of region, and unemployment duration prior to participation for the ER programme.These differences are displayed in Table 18. We find that ER improves economic outcomesof participating for younger workers, workers with histories of short-term unemployment, andthose living in rural areas compared to older workers, those with histories of long-termunemployment, and those living in urban areas, respectively.

We also find relevant subgroup differences across individuals participating in SE. Inparticular, we find that SE is more successful for females than for males, for workers withouta high-school diploma than for those with, and for workers living in rural areas compared tothose in urban areas.


20/43

retraining for different subgroups, this is likely due to the lack of precision due to small sub-sample sizes.

VIII. Cost-Benefit Analysis and Conclusion

The purpose of this study has been to analyse the effects of four ALMPs implementedin Romania during the late 1990s. Our analysis is based on data from a follow-up surveyespecially designed for this study. We use nonparametric matching estimators to control forpotential selection bias. Our analysis of program impacts reveals that three of the fourprograms (TR, SE and ER) had success in improving participants' economic outcomes. Incontrast, our analysis reveals that PE was found detrimental for the employment prospects ofits participants.

Even though this analysis has shown significant positive impacts of TR, SE, and ERprogrammes implemented in Romania in the late 1990s, the question remains as to whether

these three ALMPs were cost-effective from societys perspective.

21

While there is somescattered data on costs, the cost-benefit issue has rarely been addressed.22 Hence we nowcompare the costs per client of the ALMP with the economic benefits, as reflected inpredicted earnings.

We estimate the average cost per client served by dividing the total amount spent ineach ALMP by the number of clients served. Table 2 displays these estimates. The cost perclient served is 541.07 thousand lei for TR, 179.15 thousand lei for SE, and 123.74 thousand

lei for ER.

To estimate the benefits of the policy, we use the estimated impact of these ALMPs onthe usual average monthly earnings of their participants. We prefer using the earningsestimates over the 2000-2001 period because they are more likely to represent individualsearnings than those observed at one point in time. This amounts to an annual sum of5,393.04 thousand lei for TR, 4,783.20 thousand lei for SE (although this estimate was notstatistically significant), and 1,047.84 thousand lei for ER, which cover by far the cost per

client served. Therefore, these three policies are definitively cost-effective.23

A caveat in our cost-benefit analysis is that we did not include among potentialbenefits: (1) possible effects on labour market behaviour of the unemployed prior toparticipation, such as, intensifying job search before entering the programmes in order toavoid participation or leaving the labour force and stop collecting UB; (2) reduced criminal


21/43


22/43

REFERENCES

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Benus J., J. Rude, and S. Patrabansh. Impact of the Emergency Demobilization andReintegration Project in Bosnia & Herzegovina, Abt Associates, April 2001.

Boeri D., and Burda. Active Labor Market Programs, Job Matching, and the CzechMiracle, 1996. European Economic Review, 805-817.

Dar A., and Z. Tzannatos. Active Labor Market Programs: A Review of the Evidence fromEvaluations, 1999. Social Protection Discussion Paper no. 9901. The World Bank,Washington, D.C.

Earle, J. and C. Pauna. Incidence and Duration of Unemployment in Romania, 1996.European Economic Review 49: 829-837.

Earle, J. and C. Pauna. Long-term Unemployment, Social Assistance and Labour MarketPolicies in Romania, 1998. Empirical Economics, 23:203-235.

Eichler, M. and M.Lechner. Some Econometric Evidence on the Effectiveness of Active

Labor Market Programmes in East Germany, 2000. Working Paper no. 318. Swiss Institutefor International Economics and Applied Economic Research (SIW). Switzerland.

Fay, R.G. Enhancing the Effectiveness of Active Labor Market Policies: Evidence fromProgramme Evaluations in OECD countries, 1996. Labor Market and Social PolicyOccasional Papers, No. 18, Paris.

Fitzenberger, B., and H.Prey. Evaluating Public Sector Sponsored Training in East

Germany, 2000. Oxford Economic Papers 52, no.3: 497-520.

Gill I., and A. Dar. Cost and Effectiveness of Retraining in Hungary, 1995. InternalDiscussion Paper IP-155, Europe and Central Asia Region, The World Bank.

Heckman J and J Smith The Preprogram Earnings Dip and the Determinants of


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Katz L. Active Labor Market Policies to Expand Employment and Opportunity, 1994.Proceedings, Federal Reserve Bank of Kansas City, issue January, p. 239-322.

Kluve, J., H. Lehmann, C. Schmidt, Active Labor Market Policies in Poland: Human CapitalEnhancement, Stigmatisation, or Benefit Churning?, 1999. Journal of ComparativeEconomics, Vol 27.

Kluve, J., H. Lehmann, C. Schmidt, Disentanglin Treatment Effects of Polish Active LabourMarket Policies: Evidence from Matched Samples, 2002. Discussion Paper no. 3298, AprilLondon, UK: Center for Economic Policy Research.

Kraus, F., P. Puhani and V. Steiner. Employmnet Effects of Publicly Financed TrainingProgramsThe East Germany Experience, 1999. Jahrbucher fur Nationalokonomie undStatistik219, no. 1-2:216-48.

Kupets, O.. The Impact of Active Labor Market Policies on the Outflows fromUnemployment to Regular Jobs in Ukraine, 2000. Thesis for the degree of Master of Arts inEconomics. Nation University. Kiev Mohyla Academy.

Layard, R., S. Nickell, and R. Jackman. Unemployment, Macroeconomic Performance andthe Labor Market. 1991. Oxford: Oxford University Press.

Lehmann Harmut, Active Labor Market Policies in the OECD and in Selected Transition

Economies, 1995 Policy Research Working Paper1502, World Bank.

Lechner M. Continuous Off-the-Job Training in East Germany after Unification:Preliminary Results of an Evaluation of the Short-Run Effects for Individual Workers,1998a. In Horst Brezinski, Egon Franck and Michael Fritsch (Eds.), The microeconomics oftransformation and growth (pp. 205-30). Cheltenham, U.K. and Northampton, Mass.: Elgar;distributed by American International Distribution Corporation Williston VT.

Lechner M. Training the East German Labour Force: Microeconometric Evaluations ofContinuous Vocational Training After Unification, 1998b. Studies in ContemporaryEconomics. Heidelberg: Physica.

Lechner M. Earnings and Employment Effects of Continuous Off-the-Job Training in EastGermany After Unification 1999a Journal of Business and Economic Statistics 17 no


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Lubyova, M., van Ours, J.C. Effects of Active Labor Market Programs on the TransitionRate from Unemployment into Regular Jobs in the Slovak Republic, 1999.Journal ofComparative Economics 27, no. 466:805-37.

Martin, J.P., What Works among Active Labor Market Policies: Evidence form OECDCountries Experiences, 1998. Labor Market and Social Policy Occasional Papers No. 35.Paris.

OLeary, C. An Impact Analysis of Employment Programs in Hungary, 1995. UpjohnInstitute Technical Report No 95-30.

OLeary, C. J. Evaluating the Effectiveness of Active Labor Programs in Hungary, 1998a.Upjohn Institute Technical Report No 98-013.

OLeary, C. J. Evaluating the Effectiveness of Active Labor Programs in Poland, 1998b.Upjohn Institute Technical Report No 98-012.

OLeary, C. J., P. Kolodziejczyk, and G. Lazar. The Net Impact of Active LabourProgrammes in Hungary and Poland, 1998. International Labour Review, 137(3), 321-346.

Puhani, P. Advantage through Training? A Microeconometric Evaluation of theEmployment Effects of Active Labour Market Programmes in Poland, 1998. ZEW

Discussion Paper no. 98-25, Manheim.

Puhani P. and V. Steiner. The Effectiveness and Efficiency of ALMP in Poland, 1997.Empirica 24, p. 209-231.

Rosenbaum P., and Rubin, D. The Central Role of the Propensity Score in ObservationalStudies for Causal Effects, 1983. Biometrika 27, no.1:33-60.

Rubin, Donald B. "Matching to Remove Bias in Observational Studies." Biometrics, vol. 29,1973, pp. 159-183.

Terrell, K., and V. Sorm. Labor Market Policies and Unemployment in the CzechRepublic, 1999. Journal of Comparative Economics 27, no. 1:33-60.


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Table 1

Characteristics of ALMPs

Training and

Retraining

Self-Employment

Assistance

Public Employment Employment and

Relocation Services

Content of the

program

Vocational, generaleducation and

literacy, and formalbusiness skillstraining

Initial assessment ofthe aptitude and skills

of unemployedpersons to startbusinesses,developing businessplans, advising onaccounting, financial,legal, marketing andsales services issues,assistance in thedialogue with local

authorities, short-termentrepreneurialcourses and trainingand other consultingservices

Environmentalcleanup, ecological

projects related toinfrastructure,refurbishment ofpublic infrastructure,and provision ofassistance and supportto social agencies,such as schools, orretirement homes

Job and socialcounseling, labor

market information,job search assistance,job placementservices, andrelocation assistance

Maximum

durationa

Up to nine months No general rule, up to12 months

Up to six months Up to nine months

Participants

stipend

Subsistence stipendwas at the minimum

wage level and for aperiod equal to thedifference betweenthe months ofunemploymentbenefits and monthsof training

There were provisionsfor short-term

working capital loansof up to $25,000 U.S.dollars to programparticipants

Stipend was set at amaximum of the

average wage level ofthe type of activityprovided and for theduration of theprogram

Up to two months ofsalary at the

minimum wage.In addition, thoseclients receivingrelocation assistancecould be reimbursedfor expensesassociated withmoving to anothercommunityup to

$500 U.S. dollarsequivalent in lei perfamily, based onsubmission ofreceipts.

Target group Persons exposed tohigh risk of

Unemployedentrepreneurs who

Long-termunemployed

Recently unemployed


26/43

Table 2

Eligible Costs and Limits of Reimbursement for Service Providers

Training and

Retraining

Self-Employment

Assistance

Public Employment Employment and

Relocation Services

Eligible costs Eligible costsincluded cost of

training andadministrativepersonnel, rent andutilities, consumablematerials, clienttransportation, non-durable goods,capital depreciationup to 20 percent peryear.

Eligible costs includedpersonnel services,

transportation costs,rent and utilities,consumable materialsand non-durablegoods, and capitaldepreciation up to 20percent per year.

These public worksprojects covered the

cost of supervisorypersonnel and up to 6months of programparticipantsstipends. Othereligible costsincludedtransportation costs,related training costs,utilities, and

consumable and non-durable goods.

Eligible costsincluded staff and

administrativepersonnel costs, rentand utilities,consumablematerials, clienttransportation for jobinterviews, non-durable goods, anddepreciation ofcapital equipment up

to 20 percent peryear.

Limits of

reimbursement

The cost of trainingwas limited to $560U.S. dollars per classunit.

Costs per client had tobe specified in allcontracts, howeverunit costs could beidentified for differentcategories of services

by each serviceprovider based on theunderstanding that allclients did not needfull services and somemay drop out afterinitial contacts.Reimbursement toservice providers was

based on thecontracted averagecost per client, foreach categoryservices.

Proposingorganizations had toprovide evidence ofadministrative andfinancial viability.Local governments

and other eligibleorganizations couldpropose public worksprojects with amaximum cost of$50,000 U.S. dollars(or higher with a no-objection from theWorld Bank).

Source: USDOL Technical Assistance Support Team.


27/43

Table 3

Completed ALMP contracts as of September 1, 2001

Number

of

contracts

Clients

served

Clients

placed

Placement

rate

Total cost

(Lei)

Cost per

client

(Lei)

Cost per

placement

(Lei)

Training and

retraining

54 2,892.00 1,197 41.39% 1.564,771,985.06 541,069.15 1,307,244.77

Self-

Employment

Assistance

92 20,293.00 3,568 17.58% 3,635,562,636.30 179,153.53 1,018,935.72

Public

employment533 9,496.00 1,248 13.14% 27,688,156,974.32 2,915,770.53 22,186,023.22

Employment

and

relocation

services

88 31,679.00 6,610 20.87% 3,920,060,312.43 123,743.18 593,049.97

Costs figures have been deflated using 1998 deflator.Source: USDOL Technical Assistance Support Team.


28/43


29/43

Table 5

Baseline Employment Characteristics of

ALMP Participants and Non-Participants, 1998

(Percentages except where noted)

Training andRetraining

(1)

Self-

EmploymentAssistance

(2)

PublicEmployment

(3)

Employment

and

RelocationServices

(4)

Non-participants

(5)

Characteristics

Work Experience

Work experience (years)21.43(0.83)

22.99(0.42)

21.74(0.39)

23.99(0.30)

23.63(0.23)

1998 Employment status

Not employed in 1998 45.83 23.82 59.10 22.36 19.19

Employed in 1998 54.17 76.18 40.90 77.64 80.81Employed between 1 and 3months in 1998

4.17 1.39 5.62 4.42 2.53

Employed between 4 and 6

months in 1998

12.5 6.37 16.85 8.70 7.40


months in 1998

4.17 3.05 8.09 10.71 5.53


months in 1998

33.33 65.37 10.34 53.82 65.36

Not employed in 1998 45.83 23.82 59.10 22.36 19.19

1998 usual monthly earningsEarnings per month

Under 500 thousand lei 1.39 4.43 4.94 5.22 3.00500 - 600 thousand lei 9.72 3.05 2.02 5.22 4.46601 - 700 thousand lei 2.78 5.82 5.82 9.64 7.13701 - 850 thousand lei 6.94 13.02 6.74 14.19 12.26851 - 1,000 thousand lei 12.5 10.80 5.39 15.66 14.721,001 - 1,200 thousand lei 9.72 13.30 12.58 13.79 14.061,201 - 1,500 thousand lei 6.94 13.30 3.60 7.36 10.79

1,501 - 1,900 thousand lei 2.78 5.54 1.13 3.88 6.791,901 - 2,500 thousand lei 1.39 4.16 0.45 1.20 5.40More than 2,500 thousand lei 0 2.77 0.45 1.47 2.20

Average monthly earnings

(in thousand lei)

522.92(65.25)

881.72(39.38)

384.16(25.64)

758.07(22.51)

926.60(17.88)

Unemployment experience in 1998

A l t l th 6 26 3 38 8 75 3 90 2 99


30/43

Table 6Results from the binomial probit estimations

Training and

retraining

(1)

Self-

Employment

Assistance

(2)

Public

employment

(3)

Employment

and relocation

(4)

Characteristics

Male .1713

(.1948181)

-.2015284

(.0926006)

.4609385

(.1283769)

-.1427264

(.0725004)Age .3892

(.3195778).0284343

(.1061328).0961576

(.1062873).0140676

(.0929445)Age squared -.0047

(.0038289)-.0004043(.0012505)

-.0010315(.0012378)

-.0001519(.0010719)

Education completed

Secondary school .6765(.3441943)

.0398253(.1420994)

-.1328247(.1140381)

.0801002(.1099728)

High school .2033

(.3623174)

.3389603

(.1468737)

-.2036724

(.1386652)

-.0840283

(.1175862)University -.0648

(.490365).6136505

(.1687934)-.3965541(.2151126)

-.0083351(.1411292)

Persons in the household

Three .0475(.2794365)

.1021722(.1271709)

-.0426679(.1395565)

.0232715(.1042423)

Four -.1809(.279879)

.0459635(.1259283)

.1387877(.1311306)

.133011(.1018456)

>four -.1987(.3207308)

.0726954(.1431552)

.164182(.1377938)

.0280627(.1143186)

Respondent is the main earner -.0642(.2694773)

-.1547861(.1348952)

-.0809511(.1153289)

.0962171(.1111627)

Respondent is spouse of main earner -.0171(.2698388)

-.3095629(.1379943)

-.2172834(.1344928)

-.0487241(.1115485)

Region

Urban


31/43

Table 6 (Continued)

Results from the binomial probit estimations

Training and

retraining

(1)

Self-

Employment

Assistance

(2)

Public

employment

(3)

Employment

and relocation

(4)

Characteristics

1998 employment spell

1-3 months

-1.3069(.9093462)

-.9830641

(.499512).1871584

(.3420969)-.6807008(.3418347)

4-6 months.5223

(.8894968)-.1562037(.4336655)

.0601928(.3414572)

-.6466339(.3363872)

7-9 months-.0938

(.8874751)-.2502013(.4274598)

.2297862(.3266278)

-.3247323(.3236533)

9-12 month.6000

(.9295796)

.9910766

(.4134734)

-.1674585

(.3296845)

-.123323

(.2971646)Average earnings per month in 1998

(in thousand lei) (wage98)

-.0016(.0004077)

-.0000(.0000943)

-.0003(.0001549)

-.0001(.0000854)

500-600 1.2480(.5832753)

-.2457(.2942938)

-.6796(.3086426)

-.1813(.2095827)

601-700 .6409(.6014568)

-.1330(.249114)

-.3222(.2664017)

-.2447(.1841415)

701-850 .7412(.518917)

-.0327(.2145763)

-.2518(.2322484)

-.1748(.1698717)

851-1,000 1.1921(.4613879) -.2962(.2074279) -.1687(.2431542) -.2043(.1625509)1,001-1,200 1.0384

(.4632318)-.3793

(.1984934).4523

(.2317394)-.1763

(.1622569)1,201-1,500 1.5699

(.4753651)-.1055

(.1972956)-.2128

(.2754237)-.3851

(.1724099)1,501-1,900 1.7622

(.5583888)-.3607

(.2262893)-.1731

(.3575139)-.4094

(.1938586)1,901-2,500

n.a.-.3758

(.2408035)-.8899

(.498729)-.9456

(.2595758)

1998 average unemployment spell(months)

.6457(.1682585)

.3975(.0973285)

.2787(.0788757)

.5042(.0673983)

Avg. unemployment spell squared -.0646(.014862)

-.0289(.009252)

-.0181(.0070304)

-.0387(.0071279)

1998 unemployed at least 9 months 2.9805(1.099017)

.6637(.7353178)

.0427(.5103883)

.2608(.5406227)


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33/43

Table 8

Description of outcome variables

Variables Definition

At the time of the survey

Employed Person was employed at the time of the survey (dummy variable)Average monthly earnings Average monthly earnings at the time of the survey

During the two year period 2000-2001Employed at least 6 months Person has been employed for at least 6 months during the period

2000-2001 (dummy variable)Employed at least 12 months Person has been employed for at least 12 months during the period

2000-2001 (dummy variable)Months unemployed Number of months the person has been unemployed during the period

2000-2001Months receiving UB payments Number of months the person has been registered with the Public

Employment Services and receiving unemployment benefits paymentduring the period 2000-2001

Average monthly earnings Average monthly earnings during the two-year period 2000-2001Note: Earnings are deflated by gross domestic product (base=1998). Earnings are coded as zero if personreported not working at the time of the survey.


34/43

33

Table 9

Outcomes for ALMP Participants

(Percentages except where noted)

Training and

Retraining

Self-Employment

Assistance Public Employment

Employment

and

Relocation

OUTCOMES

Current experience

Employed 57.81 50.86 31.74 51.28Average monthly earnings (in thousand lei) 311.76 303.28 160.96 309.64

During the two year period 2000-2001

Employed for at least 6 months 75.00 78.86 48.17 78.87Employed for at least 12 months 65.62 59.71 33.56 63.39Average monthly earnings (in thousand lei) 449.42 398.60 256.12 394.34Months unemployed 9.52 10.36 16.22 9.45Months receiving UB payments 0.06 1.44 1.78 0.79

Sample size 72 362 445 747

Monthly earnings have been deflated using 1998 deflator.


35/43

34

Table 10

Average Treatment Effects of Programmes on the Employment Experience of their Participants, by ALMPs

(Percentage points except where noted)

Training andRetraining

Self-EmploymentAssistance Public Employment

Employment andRelocation

OUTCOMES

Current experience

Employed12.47

( -7..00; 29.54 )

6.14

(-0.44 12.29 )

0.61

(-6.07; 6.29 )

8.45

(3.19; 13.90 )

Average monthly earnings (in thousand lei)

65.67( -76.45; 177.64 )

37.58(-13.25; 80.12 )

3.10( -33.87; 33.44 )

56.86(1 0.49; 109.51)


Employed for at least 6 months2.53

(-10.55; 27.28)8.38

(2.29; 14.13)-7.36

( -14.98; -0.75 )6.22

( 2.35 ; 13.52 )


(-10.76; 26.91)7.97

(-0.20; 14.40)-8.45

( -15.41 -1.40 )7.65

( 2.11 ; 13.73 )

Average monthly earnings (in thousand lei)164.81

( 63.09; 362.20 )43.08

(-9.48; 87.58 )-6.65

( -47.29; 30.33 )87.32

( 56.99; 130.21 )Months unemployed -1.66

( -4.91; 2.79 )-1.82

( -3.00 -0.54 )1.95

( 0.66; 3.21 )-1.90

( -3.15 ; -0.9 2)Months receiving UB payments -1.01

( -2.24; -0.53 )-0.75

(-1.50; -0.05)0.21

( -0.60; 0.93 )-0.74

(-1.18 ; -0.29 )

Sample size 768 1,311 1,445 1,748



36/43

35

Table 11

Impacts of Training and Retraining Services


PARTICIPANTS VS. NON-PARTICIPANTSPARTICIPANTS VS. MATCHED NON-

PARTICIPANTS

Difference of MeansRegression Adjusted(using all observable

variables)

Difference of Means(using all observablevariables, with theexception of pre-

employment history, tomatch participants to

non-participants)

Difference of Means(using all observablevariables to match

participants to non-participants)

OUTCOMES

Current experience

Employed19.09(0.59)

18.10(7.80)

16.00(4.32; 28.34 )

12.47( -7.00; 29.54 )


(43.26)68.90

(43.58)21.44

(-225.22; 132.65)65.67

( -76.45; 177.64 )



(5.09)10.04(5.61)

9.09(-3.52; 2.17)

2.53(-10.55; 27.28)

Employed for at least 12 months16.09(5.60)

20.49(6.72)

17.32(4.46; 3.14)

8.06(-10.76; 26.91)

Average monthly earnings (in thousand lei)129.29(50.08)

144.70(46.01)

113.64(1.08; 212.78)

164.81( 63.09; 362.20 )

Months unemployed -3.18(1.19)

-3.99(1.28)

-3.80(-6.74; -1.09)

-1.66( -4.91; 2.79 )

Months receiving UB payments -1.74(0.14)

-1.66(0.31)

-1.93(-2.77; -1.28)

-1.01( -2.24; -0.53 )

Sample size 1,573 1,573 741 768

Sample size of the treatment group 72 72 70 61

Sample size of the comparison group 1,501 1,501 671 553



37/43

Table 12

Impacts of Self-Employment Assistance Program



PARTICIPANTS


variables)



non-participants)



OUTCOMES

Current experience

Employed12.42(2.90)

7.68(3.7)

7.52(0.01; 13.22)

6.14(-0.44 12.29 )


(22.45)40.50

(20.87)40.67

(-6.56; 84.21)37.58

(-13.25; 80.12 )



9.92(2.83)

10.27(3.91; 15.50)

8.38(2.29; 14.13)


(2.84)8.77(3.6)

9.11(2.14; 15.91)

7.97(-0.20; 14.40)


(21.91)21.09

(21.29)40.29

(-10.36; 82.64)43.08

(-9.48; 87.58 )Months unemployed -0.99

(0.28)-1.74(0.57)

-2.30(-3.52; -1.13)

-1.82( -3.00 -0.54 )


-0.75(0.33)

-1.11(-1.89; -.41)

-0.75(-1.50; -0.05)

Sample size 1,863 1,863 1,318 1,311


Sample size of the comparison group 1,501 1,501 960 961



38/43

Table 13

Impacts of Public Employment Services



PARTICIPANTS


variables)



non-participants)



OUTCOMES

Current experience

Employed-7.17(2.54)

-3.23(3.4)

-4.35(-10.20; 1.15)

0.61(-6.07; 6.29 )

Average monthly earnings (in thousand lei)-70.14(15.77)

-17.82(16.07)

-34.66(-70.70; -4.48)

3.10( -33.87; 33.44 )


Employed for at least 6 months-19.57(2.63)

-11.61(3.42)

-13.12(-19.76; -7.68)

-7.36( -14.98; -0.75 )

Employed for at least 12 months-18.11(2.55)

-13.00(3.44)

-13.06(-19.38; -7.47)

-8.45( -15.41 -1.40 )

Average monthly earnings (in thousand lei)-63.03(16.99)

-11.53(17.36)

-45.58(-80.38; -9.37)

-6.65( -47.29; 30.33 )

Months unemployed 1.33

(0.17)

2.41

(0.56)

2.99(1.90; 4.31)

1.95

( 0.66; 3.21 )Months receiving UB payments -0.1(0.09)

0.44(0.32)

0.34(-0.30; 0.99)

0.21( -0.60; 0.93 )

Sample size 1,947 1,947 1,714 1,445


Sample size of the comparison group 1,501 1,501 1,269 1,006



39/43

Table 14

Impacts of Employment and Relocation Services



PARTICIPANTS


variables)



non-participants)



OUTCOMES

Current experience

Employed12.17(2.22)

12.16(2.95)

9.81(5.25; 14.25)

8.45(3.19; 13.90 )


(20.38)78.43

(21.46)56.32

(11.92; 97.90)56.86

(1 0.49; 109.51)



9.83(2.22)

9.07(4.66; 13.18)

6.22( 2.35 ; 13.52 )


11.93(2.72)

11.24(6.29; 15.43)

7.65( 2.11 ; 13.73 )


(16.24)84.19

(15.83)62.37

(25.48; 92.84)87.32

( 56.99; 130.21 )Months unemployed -0.68

(0.11)-2.40(0.45)

-2.57(-3.39; -1.71)

-1.90( -3.15 ; -0.9 2)


-1.17(0.21)

-1.42(-1.87; -.91)

-0.74(-1.18 ; -0.29 )

Sample size 2,248 2,248 1,724 1,748


Sample size of the comparison group 1,501 1,501 978 1,005



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39

Table 15

Average treatment effects of Training and Retraining Services according to different socio-demographic characteristics


Males Females35

years old

No highschool

diploma

Highschool

diploma ormore

Unemployment5 months Rural Urban

OUTCOMES

Current experience

Employed11.90 20.73 25.64 13.58 13.81 9.30 8.51 5.32

n.a. 3.07

Average wage(in tousand lei)

89.10 76.37147.63 58.50

79.73 119.34 78.16 -52.80n.a. 13.18

During the two year period 2000-2001Employed for at least6 months

-2.72 0.92 14.01 -8.47 0.96 -0.71 11.61 6.43n.a. -6.92

Employed for at least12 months 8.17 17.24 34.42 3.11 5.75 10.08 17.63 7.98 n.a. 4.73

Average wage(in thousand lei)

173.83 116.59 230.69 103.24 194.67* 95.60 138.95 86.77n.a. 88.23

Monthsunemployment

0.25 -3.67 -6.45 -0.29 -3.09 -1.79 -3.79 -2.85n.a. -1.53

Months receiving UBpayments

-1.31* -0.51 -1.65* -1.14* -0.95* -0.82* -1.14* 0.08*n.a. -0.83*

Sample size 192 105 62 265 273 49 190 72 n.a. 375

Sample size of thetreatment group 27 27 16 38 41 14 26 19 n.a. 46

Sample size of thecomparison group

165 78 46 227 232 35 164 53 n.a. 329

Monthly earnings have been deflated using 1998 deflator.* indicates that estimates are significant at the 5% level. indicates that the difference of the two estimated effects is significant at the 5% level.Note: the number of observations does not necessarily add up to the one in the full sample.


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Table 16

Average treatment effects of Self-Employment Assistance according to different socio-demographic characteristics


Males Females35

years old

No highschool

diploma

Highschool

diploma ormore


OUTCOMES

Current experience

Employed1.18 2.83 -2.83 9.01* 5.48 5.15 4.29 18.98

9.90 4.00


8.59 23.63-51.40 58.01*

20.34 41.30 31.46 204.01*36.90 42.54


1.47 13.15* 9.35 8.31 13.45* 4.89 5.64 3.1519.89* 0.06

Employed for at least12 months 3.68 9.04 12.89 10.76* 19.35*

1.45

3.65 4.35 19.06* 5.38


-21.72 46.86 5.11 43.27 47.95 14.68 19.68 123.9010.28 34.48

Monthsunemployment

-1.03 -1.55 -2.50 -2.22*-3.61* -0.57

-1.02 -1.55-3.64* -1.20


-0.68 -1.16 -0.71 -0.75 -1.93* 6.06 -0.70 -0.01 -3.61*0.36

Sample size 790 463 273 955 595 687 966 208 427 774

Sample size of thetreatment group 181 175 97 254 200 150 244 45 142 210


609 288 176 701 395 537 722 163 285 564



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Table 18

Average treatment effects of Employment and Relocation Services according to different socio-demographic characteristics


Males Females35

years old

No highschool

diploma

Highschool

diploma ormore


OUTCOMES

Current experience

Employed8.95* 8.24* 16.89* 6.73* 5.86 11.28* 12.25* -3.83

17.93* 6.13*


85.24* 44.1965.73 60.67*

73.48 55.11* 102.01* -70.20*91.54* 47.19


6.65* 6.83 17.78* 3.96 3.87 6.47 7.55* -5.027.73 3.68*

Employed for at least12 months 8.18* 9.64* 26.20*

4.12

5.39 9.13* 7.33* -1.15 17.25* 5.09


109.04* 59.27* 116.62* 82.81* 60.08 97.01* 91.47* 18.83144.24* 50.42*

Monthsunemployment

-2.42* -1.79* -4.62* -1.21 -1.40 -1.96* -2.04* -0.20-4.87* -0.96


-0.33 -1.22* -0.66 -0.76* -0.83* -0.76 -1.00* -0.21-1.57* -0.50*

Sample size 901 804 362 1,365 977 725 1,282 324 454 1,177

Sample size of thetreatment group 338 400 159 577 438 296 482 213 189 531


563 404 203 788 539 429 1,282 111 265 646


Active Labor Programs in Romania

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